Dense 3D displacement vector fields for point cloud-based landslide monitoring

Abstract We propose a novel fully automated deformation analysis pipeline capable of estimating real 3D displacement vectors from point cloud data. Different the traditional methods that establish displacements based on proximity in Euclidean space, our approach estimates dense vector fields by searching for corresponding points across epochs space local feature descriptors. Due to this formulation, method is also sensitive motion and deformations occur parallel underlying surface. By enabling efficient processing, proposed can be applied clouds arbitrary size. compare data two landslides show while often underestimate displacements, correctly full vectors.